CFP PE-WASUN 2020

May 21st, 2020 Daniela Lopez de Luise

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Please accept our apologies if you receive multiple copies of this Call for Papers

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C a l l  F o r  P a p e r s

ACM* PE-WASUN 2020

17th ACM* International Symposium on Performance Evaluation of
Wireless Ad Hoc, Sensor, and Ubiquitous Networks
(Jointly with the 23rd ACM MSWiM Conference)

Date: November 16th– 20th, 2020

Location: Alicante (Spain)

Website: http://pewasun.upc.edu/PEWASUN2020/  

 

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Scope

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Wireless ad hoc, sensor, along with ubiquitous networks have recently witnessed their fastest growth period ever in history, and this trend is likely to continue for the foreseeable future. However, as such networks become increasingly complex, performance modelling and evaluation will play a crucial part in their design process to ensure their successful deployment and exploitation in practice.

This symposium will bring together scientists, engineers, and practitioners to share and exchange their experiences, discuss challenges, and report state-of-the-art and in-progress research on all aspects of wireless ad hoc, sensor, and ubiquitous networks with a specific emphasis on their performance evaluation and analysis.

Topics of interest include, but are not limited to:

  • Predictive performance models of ad hoc, sensor, and ubiquitous networks.
  • Probabilistic models for ad hoc, sensor and ubiquitous networks.
  • Queuing and network information theoretic analysis
  • Analytical modeling
  • Automatic performance analysis
  • Tracing and trace analysis
  • Software tools for network performance and evaluation
  • Performance measurement, evaluation and monitoring tools for ad hoc, sensor and ubiquitous networks
  • Case studies demonstrating the role of performance evaluation in the design of ad hoc, sensor and ubiquitous networks
  • Network performance improvement through optimization and tuning
  • Mobility modeling and management
  • Traffic models for ad hoc, sensor networks
  • Performance evaluation of wireless mesh networks
  • Performance evaluation of pervasive and ubiquitous networks
  • Performance evaluation of VANETs
  • Performance of wireless and sensor devices
  • Performance of spectrum agile and cognitive wireless sensor networks
  • Analysis of multimedia applications over wireless ad-hoc and sensor networks
  • Performance of pervasive computing and services
  • Analysis of mobile cloud networking and computing
  • Performance of continuity of service over heterogeneous networks, seamless connectivity
  • Analysis of security and privacy in ad hoc networks and ubiquitous networks
  • Simulation methods, performance and analysis
  • Real experimentation, deployments, open platforms

 

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Committees

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General chair

Luis J. de la Cruz Llopis           Universitat Politècnica de Catalunya, Spain (luis.delacruz@upc.edu)

Program Co-Chairs

Mónica Aguilar Igartua         Universitat Politècnica de Catalunya, Spain (monica.aguilar@upc.edu)

Carolina Tripp Barba            Universidad Autónoma de Sinaloa, México (ctripp@uas.edu.mx)

WEB/ Poster Chair

Juan Pablo Astudillo León    Universitat Politècnica de Catalunya, Spain (juan.pablo.astudillo@upc.edu)

Demo/Tools Chair

Pablo Barbecho Bautista      Universitat Politècnica de Catalunya, Spain (pablo.barbecho@upc.edu)

Publicity Chair

Leticia Lemus Cárdenas       Universitat Politècnica de Catalunya, Spain (leticia.lemux@entel.upc.edu)

Program Committee Members

http://pewasun.upc.edu/PEWASUN2020/committees.html 

 

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Paper Submission

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Authors are invited to submit their papers through EasyChair on the
following link: https://www.easychair.org/conferences/?conf=pewasun2020

The length of the papers should not exceed 8 single-spaced pages
(in two-column format), ACM style including tables and figures. A template
for ACM SIG Proceedings style (LaTeX2e and MS Word) can be found at

https://www.acm.org/publications/proceedings-template
Accepted papers will appear in the ACM symposium proceedings.

The authors of accepted papers must guarantee that their paper will be
presented at the Symposium. At least one author of each accepted paper
must be registered for the symposium, in order for that paper to appear
in the proceedings and to be scheduled for presentation.

 

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Important Dates

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Full paper due: July 5th,  2020
Acceptance notification: August 15th, 2020
Camera ready due: TBA
Speaker Author Registration: TBA
Symposium: November 16th – 20th, 2020 – Jointly with MSWiM'20

 

Yours sincerely,

PE-WASUN 2020 Committee

http://pewasun.upc.edu/PEWASUN2020  

 
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Entropy – CfP: Special Issue on “Statistical Machine Learning for Multimodal Data Analysis”

May 21st, 2020 Daniela Lopez de Luise

Entropy Journal (Impact Factor 2.419)

 

Special Issue on “Statistical Machine Learning for Multimodal Data Analysis”

 

Methods and algorithms in statistical machine learning explore relationships between variables in large, complex datasets in supervised, unsupervised or semi-supervised manners. Significant research results have been presented in recent years on a variety of topics, including linear and nonlinear regression, classification, clustering, resampling methods, model selection, and regularization. Furthermore, the latest strides in deep, reinforcement, and adversarial learning in conjunction with increasing availability of data from a wide variety of modalities (visual, thermal, hyperspectral, audio/speech, textual, radar, network traffic, energy, Channel State Information, and others) provide great opportunities and at the same time significant challenges for theoretical advancements and novel practical developments in a variety of application domains.

 

This Special Issue solicits original research papers as well as review articles and short communications in the above-described areas. Topics of interest include, without being limited to, the following:

  • Statistical machine learning and pattern recognition techniques for fusion and/or understanding of multimodal, multisensorial, and/or heterogeneous data;
  • Deep learning and reinforcement learning for multimodal data and signal analysis;
  • Generative adversarial networks for multimodal data analysis;
  • Optimization methods for training of statistical models and tuning of hyperparameters;
  • Quantitative evaluation, comparison and benchmarking of statistical learning methods;
  • Statistical methods for handling class imbalance and data irregularities;
  • Explainability and interpretability in statistical machine learning;
  • Applications of statistical machine learning in real-world problems.

 

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

 

Keywords

  • Statistical machine learning
  • Pattern recognition
  • Deep learning
  • Reinforcement learning
  • Adversarial learning
  • Hyperparameter optimization
  • Multisensorial data fusion and analysis
  • Multimodal signal processing
  • Explainability and interpretability in machine learning

 

Deadline for manuscript submissions: 1 November 2020

 

Special Issue Guest Editor

Dr. Athanasios Voulodimos

Assistant Professor

Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece

 

Special Issue Webpage: https://www.mdpi.com/journal/entropy/special_issues/Stat_ML_Data

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VISMAC2020 summer school – COVID-19 Updates

May 21st, 2020 Daniela Lopez de Luise
First of all we would like to thank all those who have already expressed
interest in attending the VISMAC summer school. Unfortunately, we are
living in very worrying and uncertain times which do not allow the event
to take place in safety now.

We monitor the situation to safeguard everyone, hence we are obliged to
postpone VISMAC to when sanitary conditions will allow it. Regrettably,
it will not take place before June 2021, but we will give immediate
communication as soon as the situation is clearly stable and secure.

Thank you for your kind understanding and take care of yourselves,

Domenico Tegolo, UNIPA
Cesare Valenti, UNIPA
Roberto Pirrone, UNIPA
Filippo Stanco, UNICT
Marco E. Tabacchi, UNIPA
Fabio Bellavia, UNIPA

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FATE/MM (ACM MM 2020 Workshop on Fairness, Accountability, Transparency and Ethics in MultiMedia)

May 21st, 2020 Daniela Lopez de Luise

;text-indent:0px;text-transform:none;white-space:normal;word-spacing:0px;text-decoration-style:initial;text-decoration-color:initial;background-color:#fdfdfd”>Important Dates

  • Paper submission, July 30th
  • Author Notification, August, 26th
  • Camera-Ready, September 2nd
  • Workshop, October

All deadlines are at midnight (23:59) Anywhere on Earth.

Call for Contributions

The workshop aims to foster research around a timely and crucial topic for the present digitized society: the fairness, accountability, transparency and ethics of multimedia algorithms. The workshop has a strong scientific link with the FAT/ML workshop, satellite of ICML, and the ACM FAT* conference. Differently from FAT/ML, which is anchored in machine learning, the FATE/MM workshop addresses fairness, accountability, transparency and ethics in multimedia processing, retrieval, categorization and applications. More precisely, we expect submissions covering any topic closely related to the multimedia community AND falling in one (or many) of the following categories:

Models

  • Techniques and models for fairness-aware multimedia modeling, multimedia information retrieval, and recommendation.
  • Interpretable and explainable models in multimedia.
  • Models and frameworks for conducting FATE audits of multimedia systems.
  • Models for addressing inclusion and exclusion in multimedia.

Algorithm evaluation

  • Qualitative, quantitative, and experimental studies on subjective perceptions of algorithmic bias, unfairness and ethical issues.
  • Experimental results of FATE audits of multimedia systems.
  • Objective metrics for measuring unfairness and bias in multimedia.
  • Generation of human-readable explanations for multimedia models and algorithmic outputs.
  • Metrics for measuring inclusiveness in multimedia systems.

Data collection and curation

  • Defining, measuring and mitigating problematic biases in multimedia datasets.
  • Ethical issues in multimedia data collection processes.
  • Improvement of data collection processes to be more fair, diverse, and inclusive.
  • Data collection regarding potential unfairness in systems and ethical consequences.

Applications

  • Research on fair and transparent multimedia tools and applications
  • Ethical design and/or usage of multimedia tools and applications
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iMIMIC 2020 – Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020

May 21st, 2020 Daniela Lopez de Luise
CALL FOR PAPERS: iMIMIC @ MICCAI 2020
Workshop on Interpretability of Machine Intelligence in Medical Image Computing at MICCAI 2020
iMIMIC 2020 workshop: October 4 2020, Lima, Peru, (https://imimic-workshop.com)
MICCAI 2020 conference: October 4-8, 2020, Lima, Peru, (https://www.miccai2020.org/)
OVERVIEW
The annual MICCAI conference attracts world leading biomedical scientists, engineers, and clinicians from a wide range of disciplines associated with medical imaging and computer assisted intervention.
Machine learning (ML) systems are achieving remarkable performances at the cost of increased complexity. Hence, they become less interpretable, which may cause distrust. As these systems are pervasively being introduced to critical domains, such as medical image computing and computer assisted intervention (MICCAI), it becomes imperative to develop methodologies to explain their predictions. Such methodologies would help physicians to decide whether they should follow/trust a prediction or not. Additionally, it could facilitate the deployment of such systems, from a legal perspective. Ultimately, interpretability is closely related with AI safety in healthcare.
However, there is very limited work regarding interpretability of ML systems among the MICCAI research. Besides increasing trust and acceptance by physicians, interpretability of ML systems can be helpful during method development. For instance, by inspecting if the model is learning aspects coherent with domain knowledge, or by studying failures. Also, it may help revealing biases in the training data, or identifying the most relevant data (e.g., specific MRI sequences in multi-sequence acquisitions). This is critical since the rise of chronic conditions has led to a continuous growth in usage of medical imaging, while at the same time reimbursements have been declining. Hence, improved productivity through the development of more efficient acquisition protocols is urgently needed.
The Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 2020 aims at introducing the challenges & opportunities related to the topic of interpretability of ML systems in the context of MICCAI.
SCOPE
Interpretability can be defined as an explanation of the machine learning system. It can be broadly defined as global, or local. The former explains the model and how it learned, while the latter is concerned with explaining individual predictions. Visualization is often useful for assisting the process of model interpretation. The model’s uncertainty may be seen as a proxy for interpreting it, by identifying difficult instances. Still, although we can find some approaches for tackling machine learning interpretability, there is a lack of formal and clear definition and taxonomy, as well as general approaches. Additionally, interpretability results often rely on comparing explanations with domain knowledge. Hence, there is the need for defining objective, quantitative, and systematic evaluation methodologies.
Covered topics include but are not limited to:
– Definition of interpretability in context of medical image analysis.
– Visualization techniques useful for model interpretation in medical image analysis.
– Local explanations for model interpretability in medical image analysis.
– Methods to improve transparency of machine learning models commonly used in medical image analysis.
– Textual explanations of model decisions in medical image analysis.
– Uncertainty quantification in context of model interpretability.
– Quantification and measurement of interpretability.
– Legal and regulatory aspects of model interpretability in medicine.
IMPORTANT DATES
Submission Deadline: June 30 2020.
Notification of Acceptance: July 21 2020.
Camera-ready Deadline: July 31 2020.
Workshop: October 4 2020.
KEYNOTE SPEAKERS
Himabindu Lakkaraju, Harvard University, USA.
Wojciech Samek, Fraunhofer HHI, Germany.
VENUE
The iMIMIC workshop will be held in the morning of 4 of October as a workshop of MICCAI 2020.
We would like to inform you that in light of the ongoing COVID-19 pandemic, the MICCAI 2020 Conference Organizing team and the MICCAI Society Board have decided to hold the MICCAI 2020 annual meeting planned for October 4-8, 2020 in Lima, Peru as a fully virtual conference. More information regarding the venue can be found at the conference website at (https://www.miccai2020.org/en/CONFERENCE-VENUE.html)
ADDITIONAL INFORMATION AND SUBMISSION DETAILS
Submissions must be original and not published elsewhere. Authors should prepare a manuscript of 8 pages, excluding references. The manuscript should be formatted according to the Lecture Notes in Computer Science (LNCS) style. All submissions will be reviewed by 3 reviewers. The reviewing process will be single-blinded. Authors will be asked to disclose possible conflict of interests, such as cooperation in the previous two years. Moreover, care will be taken to avoid reviewers from the same institution as the authors. The selection of the papers will be based on their relevance for medical image analysis, significance of results, technical and experimental merit, and clear presentation.
Authors should submit their articles in a single pdf file in the submission website – ? no later than June 30 2020.
Notification of acceptance will be sent by July 21 2020. and the camera-ready version of the papers revised according to the reviewers comments should be submitted by July 31 2020.
We intend to join the MICCAI Satellite Events joint proceedings, and publish the accepted papers as LNCS. We are also considering making the pre-print of the accepted papers publicly available.
ORGANIZING COMMITTEE
Jaime S. Cardoso, INESC TEC and University of Porto, Portugal.
Pedro H. Abreu, CISUC and University of Coimbra, Portugal.
Ivana Isgum, Amsterdam University Medical Center, The Netherlands
José P. Amorim, CISUC and University of Coimbra, Portugal – Publicity Chair
Wilson Silva, INESC TEC and University of Porto, Portugal – Program Chair
Ricardo Cruz, INESC TEC and University of Porto, Portugal – Sponsor Chair
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